Page-Level Genre Metadata for English-Language Volumes in HathiTrust, 1700-1922

The Longue Durée of Literary Prestige

Abstract: A history of literary prestige needs to study both works that achieved distinction and the mass of volumes from which they were distinguished. To understand how those patterns of preference changed across a century, we gathered two samples of English-language poetry from the period 1820–1919: one drawn from volumes reviewed in prominent periodicals and one selected at random from a large digital library (in which the majority of authors are relatively obscure). The stylistic differences associated with literary prominence turn out to be quite stable: a statistical model trained to distinguish reviewed from random volumes in any quarter of this century can make predictions almost as accurate about the rest of the period. The “poetic revolutions” described by many histories are not visible in this model; instead, there is a steady tendency for new volumes of poetry to change by slightly exaggerating certain features that defined prestige in the recent past.

The literary uses of high-dimensional space:

Abstract: Debates over “Big Data” shed more heat than light in the humanities, because the term ascribes new importance to statistical methods without explaining how those methods have changed. What we badly...

Unsupervised clustering with smoothing for detecting paratext boundaries in scanned documents

Abstract: Digital humanities scholars are developing new techniques of literary study using non-consumptive processing of large collections of scanned text. A crucial step in working with such collections is to separate the main text of a work from the surrounding paratext, the content of which may distort word counts, location references, sentiment scores, and other important outputs. Simple heuristic methods have been devised, but are not accurate for some texts and some methodological needs. This study describes a method for paratext detection based on smoothed unsupervised clustering. We show that this method is more accurate than simple heuristics, especially for non-fiction works, and edited works with larger amounts of paratext. We also show that a more accurate detection of paratext boundaries improves the accuracy of subsequent text processing, as exemplified by a readability metric.